DPROT is a bi-layer cascade Support
Vector Machine (SVM). This method involves the use of bi-layer. The
first layer SVM classifiers was trained and optimized with different
individual protein sequence features like amino acid composition,
dipeptide composition (occurrences of the possible pairs of i and i+1
amino acid residues), higher order dipeptide composition (pairs of i and
i+2 residues) and PSSM (Position Specific Scoring Matrix) composition
and secondary structure composition. Cascade module based on these
features encapsulates comprehensive information that contributes to
effective prediction.

A five-fold cross-validation technique was used for the
evaluation of various prediction strategies in the current work. The
results from the first layer were cascaded to the second layer SVM
classifier to train and generate the final classifier.

The cascade SVM classifier was able to achieve 80.5, 96.8,
94.6 and 0.77, corresponding to sensitivity, specificity, accuracy and
MCC respectively.